A study of intra-seasonal variations in the subsurface water temperatures in the South China Sea

Through analysis of the results of a verified high-fidelity numerical model, the intra-seasonal variations(ISVs) in the depth of the 22°C isotherm(D22) in the South China Sea(SCS) basin are investigated. The results show that the ISVs in the D22 exhibit distinct seasonality in the SCS. The ISVs in the D22 are quite significant, especially within a band along the northwestern boundary of the basin and at the southern end of the basin during boreal winter. In these areas, the ratio of the standard deviations(STDs) of intra-seasonal band to the STDs of total data could exceed 0.6. Although the ISVs in the D22 are detectable in the area affected by the Vietnam Offshore Current during boreal summer and autumn, these variations are sometimes overwhelmed by oscillations with other frequencies. An analysis of the causes of the ISVs in the D22 in the SCS indicates that sea surface fluxes and wind stirring are not the dominant external driving mechanisms of the phenomena described above. The ISVs in the D22 are thought to be induced mainly by the thermodynamic adjustment of the ocean itself and the associated instabilities. The energy of the northern and southern bands that display strong ISVs in the D22 may be derived from eddy kinetic energy, rather than eddy available potential energy. The diversity of the propagation of the ISVs in the D22 is very conspicuous within these two bands.

Wind power prediction based on variational mode decomposition multi-frequency combinations

Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.